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This paper presents FastFit, a novel neural vocoder architecture thatreplaces the U-Net encoder with multiple short-time Fourier transforms (STFTs)to achieve faster generation rates without sacrificing sample quality. Wereplaced each encoder block with an STFT, with parameters equal to the temporalresolution of each decoder block, leading to the skip connection. FastFitreduces the number of parameters and the generation time of the model by almosthalf while maintaining high fidelity. Through objective and subjectiveevaluations, we demonstrated that the proposed model achieves nearly twice thegeneration speed of baseline iteration-based vocoders while maintaining highsound quality. We further showed that FastFit produces sound qualities similarto those of other baselines in text-to-speech evaluation scenarios, includingmulti-speaker and zero-shot text-to-speech.

FastFit: Towards Real-Time Iterative Neural Vocoder by Replacing U-Net Encoder With Multiple STFTs